no code implementations • 26 Nov 2017 • Lingjing Wang, Yi Fang
Recent advancements in deep learning opened new opportunities for learning a high-quality 3D model from a single 2D image given sufficient training on large-scale data sets.
1 code implementation • 2 Apr 2019 • Lingjing Wang, Jianchun Chen, Xiang Li, Yi Fang
In contrast, the proposed point registration neural network (PR-Net) actively learns the registration pattern as a parametric function from a training dataset, consequently predict the desired geometric transformation to align a pair of point sets.
3 code implementations • 7 Jun 2019 • Lingjing Wang, Xiang Li, Jianchun Chen, Yi Fang
In contrast to previous efforts (e. g. coherent point drift), CPD-Net can learn displacement field function to estimate geometric transformation from a training dataset, consequently, to predict the desired geometric transformation for the alignment of previously unseen pairs without any additional iterative optimization process.
no code implementations • 14 Oct 2019 • Xiang Li, Mingyang Wang, Congcong Wen, Lingjing Wang, Nan Zhou, Yi Fang
Based on this convolution module, we further developed a multi-scale fully convolutional neural network with downsampling and upsampling blocks to enable hierarchical point feature learning.
no code implementations • 16 Oct 2019 • Jifei Wang, Lingjing Wang
This paper studies deep learning methodologies for portfolio optimization in the US equities market.
1 code implementation • NeurIPS 2019 • Jianchun Chen, Lingjing Wang, Xiang Li, Yi Fang
To address this issue, we present an end-to-end trainable deep neural networks, named Arbitrary Continuous Geometric Transformation Networks (Arbicon-Net), to directly predict the dense displacement field for pairwise image alignment.
no code implementations • CVPR 2020 • Lingjing Wang, Xiang Li, Yi Fang
In comparison, we propose a novel 3D shape segmentation method that requires few labeled data for training.
no code implementations • 10 Jun 2020 • Xiang Li, Lingjing Wang, Yi Fang
Recent studies have shown the benefits of using additional elevation data (e. g., DSM) for enhancing the performance of the semantic segmentation of aerial images.
no code implementations • 11 Jun 2020 • Lingjing Wang, Xiang Li, Yi Fang
Moreover, for a pair of source and target point sets, existing deep learning mechanisms require explicitly designed encoders to extract both deep spatial features from unstructured point clouds and their spatial correlation representation, which is further fed to a decoder to regress the desired geometric transformation for point set alignment.
no code implementations • 17 Jun 2020 • Lingjing Wang, Yi Shi, Xiang Li, Yi Fang
Global registration of point clouds aims to find an optimal alignment of a sequence of 2D or 3D point sets.
no code implementations • 25 Jul 2020 • Lingjing Wang, Xiang Li, Yi Fang
More specifically, for a given group we first define an optimizable Group Latent Descriptor (GLD) to characterize the gruopwise relationship among a group of point sets.
no code implementations • 13 Aug 2020 • Hao Huang, Jianchun Chen, Xiang Li, Lingjing Wang, Yi Fang
Recent works introduce convolutional neural networks (CNNs) to extract high-level feature maps and find correspondences through feature matching.
no code implementations • 11 Sep 2020 • Xiang Li, Lingjing Wang, Yi Fang
To bridge the performance gaps between partial point set registration with full point set registration, we proposed to incorporate a shape completion network to benefit the registration process.
no code implementations • 29 Sep 2020 • Lingjing Wang, Xiang Li, Yi Fang
Point cloud registration is the process of aligning a pair of point sets via searching for a geometric transformation.
no code implementations • 21 Oct 2020 • Hao Huang, Lingjing Wang, Xiang Li, Yi Fang
In this paper, we propose a novel meta-learning based 3D point signature model, named 3Dmetapointsignature (MEPS) network, that is capable of learning robust point signatures in 3D shapes.
no code implementations • 22 Oct 2020 • Lingjing Wang, Yu Hao, Xiang Li, Yi Fang
In this paper, we propose a meta-learning based 3D registration model, named 3D Meta-Registration, that is capable of rapidly adapting and well generalizing to new 3D registration tasks for unseen 3D point clouds.
no code implementations • 7 Jul 2021 • Xiang Li, Lingjing Wang, Yi Fang
To achieve this, we treat the shape segmentation as a point labeling problem in the metric space.